Systems and methods for determining respiratory effort

ABSTRACT

Systems and methods for calculating a measure of respiratory effort of a subject are provided. The measure of respiratory effort may be calculated based on a differential pulse transit time (DPTT) calculated for received photoplethysmograph signals. The systems and methods may allow for the calculation of respiratory effort in absolute units, and without the need for calibrations from a device that measures blood pressure (e.g., a non-invasive blood pressure cuff).

SUMMARY

Continuous non-invasive blood pressure (CNIBP) monitoring systems allowa patient's blood pressure to be tracked continuously, unlike standardocclusion cuff techniques, and without the hazards of invasive arteriallines. Some such systems use multiple pulse oximetry sensors located tomeasure photoplethysmograph (PPG) signals at multiple body sites on apatient. The resulting multiple PPG signals may be compared against eachother to estimate the patient's blood pressure. Chen et al. U.S. Pat.No. 6,599,251, which is hereby incorporated by reference herein in itsentirety, discloses some techniques for continuous and non-invasiveblood pressure monitoring using two probes or sensors that may be usedin conjunction with the present disclosure.

A differential pulse transit time (DPTT) may be determined based on thereceived PPG signals, A DPTT may represent the difference in the arrivaltimes of a portion of a cardiac wave between the two locations, and maybe determined based on identifying a corresponding fiducial point ineach of the two PPG signals (e.g., a maximum, minimum, or a notch).

The respiratory effort of a patient may be determined based on DPTTs andrelying upon a blood pressure calibration measurement from a patientusing additional equipment, such as a non-invasive blood pressure cuff.These calibrations may not be practical for a patient who has a limitedrange of motion, or who is not able to accommodate the cuff. Inaddition, these calibrations may be compromised because the patient isaware that he or she is being monitored. Accordingly, techniques thatallow for the measurement of respiratory effort without such calibrationmeasurements are needed.

In an embodiment, respiratory effort may be determined based oncharacteristic points of DPTT signals—e.g., the maximum and minimum DPTTvalues over a single respiration cycle, or any suitable window of time.The difference between the maximum and minimum DPTT values may then beused to determine the change in blood pressure of a patient. Calculatedchanges in diastolic and systolic blood pressure measurements may beused in combination to obtain a change in mean arterial pressure. Inanother embodiment, one or more mean DPTT measurements may be calculatedand used to determine a change in blood pressure over particular periodsof time For example, an increase in the baseline (i.e., mean DPTT) of aDPTT signal over time may indicate increased autonomic and/orrespiratory activity on behalf of the patient being monitored, and thusan increase in the patient's blood pressure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings and in which:

FIG. 1 shows an illustrative pulse oximetry system in accordance with anembodiment;

FIG. 2 is a block diagram of the illustrative pulse oximetry system ofFIG. 1 coupled to a patient in accordance with an embodiment;

FIG. 3 is an illustrative processing system in accordance with anembodiment;

FIG. 4 shows an illustrative PPG signal in accordance with anembodiment;

FIGS. 5( a) and 5(b) show illustrative plots of DPTT measurements inaccordance with embodiments of the disclosure;

FIG. 6 shows an illustrative plot of a mean DPTT measurement over afirst period of time in accordance with an embodiment;

FIG. 7 shows an illustrative plot of mean DPTT measurements overmultiple periods of time in accordance with an embodiment of thedisclosure;

FIG. 8 is a flowchart of an illustrative process for computing aphysical parameter based on a localized change in DPTT in accordancewith an embodiment; and

FIG. 9 is a flowchart of an illustrative process for computing anabsolute blood pressure measurement based on a localized change in DPTTin accordance with an embodiment.

DETAILED DESCRIPTION

Some CNIBP monitoring techniques utilize two probes or sensorspositioned at two different locations on a subject's body. The elapsedtime, T, between the arrivals of corresponding points of a pulse signalat the two locations may then be determined using signals obtained bythe two probes or sensors. The estimated blood pressure, p, may then berelated to the elapsed time, T, byp=a+b·ln(T)  (1)where a and b are constants that may be dependent upon the nature of thesubject and the nature of the signal detecting devices. Other suitableequations using an elapsed time between corresponding points of a pulsesignal may also be used to derive an estimated blood pressuremeasurement.

Equation (1) may be used to determine the estimated blood pressure fromthe time difference, T, between corresponding points of a pulse signalreceived by two sensors or probes attached to two different locations ofa subject. As described in more detail below, however, the value usedfor the time difference, T, in equation (1) (or in any other bloodpressure equation using an elapsed time value between correspondingpoints of a pulse signal) may also be derived from a signal obtainedfrom a single sensor or probe. In one suitable approach, the signalobtained from the single sensor or probe may take the form of a PPGsignal obtained, for example, from a CNIBP monitoring system or pulseoximeter.

A PPG signal may be used to determine blood pressure according to thepresent disclosure at least in part because the shape of the PPG signalmay be considered to be made up of the pulse wave and its manyreflections throughout the circulatory system. As such, blood pressureequations used in continuous blood pressure monitoring techniques thatuse sensors or probes at two locations (e.g., equation (1) above) mayalso be used with continuous blood pressure monitoring techniques thatuse only a single probe. As described in more detail below,characteristic points may be identified in a detected PPG signal. Todetermine blood pressure using a PPG signal, the time difference, T, inequation (1) (or in any other blood pressure equation using the timebetween corresponding points of a pulse signal) may then be substitutedwith the time between two characteristic points in a detected PPGsignal.

FIG. 1 is a perspective view of an embodiment of a CNIBP monitoringsystem 10 that may also be used to perform pulse oximetry. System 10 mayinclude a sensor 12 and a monitor 14. Sensor 12 may include an emitter16 for emitting light at one or more wavelengths into a patient'stissue. A detector 18 may also be provided in sensor 12 for detectingthe light originally from emitter 16 that emanates from the patient'stissue after passing through the tissue.

According to another embodiment and as will be described, system 10 mayinclude a plurality of sensors forming a sensor array in lieu of singlesensor 12. Each of the sensors of the sensor array may be acomplementary metal oxide semiconductor (CMOS) sensor. Alternatively,each sensor of the array may be charged coupled device (CCD) sensor. Inanother embodiment, the sensor array may be made up of a combination ofCMOS and CCD sensors. The CCD sensor may comprise a photoactive regionand a transmission region for receiving and transmitting data whereasthe CMOS sensor may be made up of an integrated circuit having an arrayof pixel sensors. Each pixel may have a photodetector and an activeamplifier.

According to an embodiment, emitter 16 and detector 18 may be onopposite sides of a digit such as a finger or toe, in which case thelight that is emanating from the tissue has passed completely throughthe digit. In an embodiment, detector 18 (e.g., a reflective sensor) maybe positioned anywhere a strong pulsatile flow may be detected (e.g.,over arteries in the neck, wrist, thigh, ankle, ear, or any othersuitable location). In an embodiment, emitter 16 and detector 18 may bearranged so that light from emitter 16 penetrates the tissue and isreflected by the tissue into detector 18, such as a sensor designed toobtain pulse oximetry or CNIBP data from a patient's forehead.

In an embodiment, the sensor or sensor array may be connected to anddraw its power from monitor 14 as shown. In another embodiment, thesensor may be wirelessly connected to monitor 14 and include its ownbattery or similar power supply (not shown). Monitor 14 may beconfigured to calculate physiological parameters (e.g., blood pressure)based at least in part on data received from sensor 12 relating to lightemission and detection. In an alternative embodiment, the calculationsmay be performed on the monitoring device itself and the result of thelight intensity reading may be passed to monitor 14. Further, monitor 14may include a display 20 configured to display the physiologicalparameters or other information about the system. In the embodimentshown, monitor 14 may also include a speaker 22 to provide an audiblesound that may be used in various other embodiments, such as forexample, sounding an audible alarm in the event that a patient'sphysiological parameters are not within a predefined normal range.

In an embodiment, sensor 12, or the sensor array, may be communicativelycoupled to monitor 14 via a cable 24. However, in other embodiments, awireless transmission device (not shown) or the like may be used insteadof or in addition to cable 24.

In the illustrated embodiment, system 10 may also include amulti-parameter patient monitor 26. The monitor may be cathode ray tubetype, a flat panel display (as shown) such as a liquid crystal display(LCD) or a plasma display, or any other type of monitor now known orlater developed. Multi-parameter patient monitor 26 may be configured tocalculate physiological parameters and to provide a display 28 forinformation from monitor 14 and from other medical monitoring devices orsystems (not shown). For example, multi-parameter patient monitor 26 maybe configured to display an estimate of a patient's blood pressure frommonitor 14, blood oxygen saturation generated by monitor 14 (referred toas an “SpO₂” measurement), and pulse rate information from monitor 14.

Monitor 14 may be communicatively coupled to multi-parameter patientmonitor 26 via a cable 32 or 34 that is coupled to a sensor input portor a digital communications port, respectively and/or may communicatewirelessly (not shown). In addition, monitor 14 and/or multi-parameterpatient monitor 26 may be coupled to a network to enable the sharing ofinformation with servers or other workstations (not shown), Monitor 14may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

Calibration device 80, which may be powered by monitor 14, a battery, orby a conventional power source such as a wall outlet, may include anysuitable blood pressure calibration device. For example, calibrationdevice 80 may take the form of any invasive or non-invasive bloodpressure monitoring or measuring system used to generate reference bloodpressure measurements for use in calibrating the CNIBP monitoringtechniques described herein. Such calibration devices may include, forexample, an aneroid or mercury sphygmomanometer and occluding cuff, apressure sensor inserted directly into a suitable artery of a patient,an oscillometric device or any other device or mechanism used to sense,measure, determine, or derive a reference blood pressure measurement. Inone suitable approach, calibration device 80 may include a manual inputdevice (not shown) used by an operator to manually input reference bloodpressure measurements obtained from some other source (e.g., an externalinvasive or non-invasive blood pressure measurement system).

Calibration device 80 may also access reference blood pressuremeasurements stored in memory (e.g., RAM, ROM, or a storage device). Forexample, in one suitable approach, calibration device 80 may accessreference blood pressure measurements from a relational database storedwithin calibration device 80, monitor 14, or multi-parameter patientmonitor 26. As described in more detail below, the reference bloodpressure measurements generated or accessed by calibration device 80 maybe updated in real-time, resulting in a continuous source of referenceblood pressure measurements for use in continuous or periodiccalibration. Alternatively, reference blood pressure measurementsgenerated or accessed by calibration device 80 may be updatedperiodically, and calibration may be performed on the same periodiccycle. In the depicted embodiments, calibration device 80 is connectedto monitor 14 via cable 82. In other embodiments, calibration device 80may be a stand-alone device that may be in wireless communication withmonitor 14. Reference blood pressure measurements may then be wirelesslytransmitted to monitor 14 for use in calibration. In still otherembodiments, calibration device 80 is completely integrated withinmonitor 14. Alternative, in other embodiments, calibration device 80 isomitted from CNIBP monitoring system 10.

FIG. 2 is a block diagram of a CNIBP monitoring system, such as system10 of FIG. 1, which may be coupled to a patient 40 in accordance with anembodiment. Certain illustrative components of sensor 12 and monitor 14are illustrated in FIG. 2. Sensor 12 may include emitter 16, detector18, and encoder 42. In the embodiment shown, emitter 16 may beconfigured to emit at least one wavelength of light (e.g., RED or IR)into a patient's tissue 40. For calculating SpO₂, emitter 16 may includea RED light emitting light source such as RED light emitting diode (LED)44 and an IR light emitting light source such as IR LED 46 for emittinglight into the patient's tissue 40. In other embodiments, emitter 16 mayinclude a light emitting light source of a wavelength other than RED orIR. In one embodiment, the RED wavelength may be between about 600 nmand about 700 nm, and the IR wavelength may be between about 800 nm andabout 1000 nm. In embodiments where a sensor array is used in place ofsingle sensor, each sensor may be configured to emit a singlewavelength. For example, a first sensor emits only a RED light while asecond only emits an IR light.

It will be understood that, as used herein, the term “light” may referto energy produced by radiative sources and may include one or more ofultrasound, radio, microwave, millimeter wave, infrared, visible,ultraviolet, gamma ray or X-ray electromagnetic radiation. As usedherein, light may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and thatany suitable wavelength of electromagnetic radiation may be appropriatefor use with the present techniques. Detector 18 may be chosen to bespecifically sensitive to the chosen targeted energy spectrum of theemitter 16.

In an embodiment, detector 18 may be configured to detect the intensityof light at the emitted wavelengths (or any other suitable wavelength).Alternatively, each sensor in the array may be configured to detect anintensity of a single wavelength. In operation, light may enter detector18 after passing through the patient's tissue 40. Detector 18 mayconvert the intensity of the received light into an electrical signal.The light intensity is directly related to the absorbance and/orreflectance of light in the tissue 40. That is, when more light at acertain wavelength is absorbed, reflected or scattered, less light ofthat wavelength is received from the tissue by the detector 18. Afterconverting the received light to an electrical signal, detector 18 maysend the signal to monitor 14, where physiological parameters may becalculated based on the absorption of one or more of the RED and JR (orother suitable) wavelengths in the patient's tissue 40.

In an embodiment, encoder 42 may contain information about sensor 12,such as what type of sensor it is (e.g., whether the sensor is intendedfor placement on a forehead or digit) and the wavelength or wavelengthsof light emitted by emitter 16. This information may be used by monitor14 to select appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This information mayallow monitor 14 to determine, for example, patient-specific thresholdranges in which the patient's physiological parameter measurementsshould fall and to enable or disable additional physiological parameteralgorithms, Encoder 42 may, for instance, be a coded resistor whichstores values corresponding to the type of sensor 12 or the type of eachsensor in the sensor array, the wavelength or wavelengths of lightemitted by emitter 16 on each sensor of the sensor array, and/or thepatient's characteristics. In another embodiment, encoder 42 may includea memory on which one or more of the following information may be storedfor communication to monitor 14: the type of the sensor 12; thewavelength or wavelengths of light emitted by emitter 16; the particularwavelength each sensor in the sensor array is monitoring; a signalthreshold for each sensor in the sensor array; any other suitableinformation; or any combination thereof.

In an embodiment, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50, Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to a light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for the RED LED 44and the IR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through an amplifier 62 and a switching circuit 64. Thesesignals are sampled at the proper time, depending upon which lightsource is illuminated. The received signal from detector 18 may bepassed through an amplifier 66, a low pass filter 68, and ananalog-to-digital converter 70. The digital data may then be stored in aqueued serial module (QSM) 72 (or buffer) for later downloading to RAM54 as QSM 72 fills up. In one embodiment, there may be multiple separateparallel paths having amplifier 66, filter 68, and A/D converter 70 formultiple light wavelengths or spectra received.

In an embodiment, microprocessor 48 may determine the patient'sphysiological parameters, such as blood pressure, SpO₂, and pulse rate,using various algorithms and/or look-up tables based on the value of thereceived signals and/or data corresponding to the light received bydetector 18. Signals corresponding to information about patient 40, andparticularly about the intensity of light emanating from a patient'stissue over time, may be transmitted from encoder 42 to a decoder 74.These signals may include, for example, encoded information relating topatient characteristics. Decoder 74 may translate these signals toenable the microprocessor to determine the thresholds based onalgorithms or look-up tables stored in ROM 52. User inputs 56 may beused to enter information about the patient, such as age, weight,height, diagnosis, medications, treatments, and so forth. In anembodiment, display 20 may exhibit a list of values which may generallyapply to the patient, such as, for example, age ranges or medicationfamilies, which the user may select using user inputs 56.

The optical signal through the tissue can be degraded by noise, amongother sources. One source of noise is ambient light that reaches thelight detector. Another source of noise is electromagnetic coupling fromother electronic instruments. Movement of the patient also introducesnoise and affects the signal. For example, the contact between thedetector and the skin, or the emitter and the skin, can be temporarilydisrupted when movement causes either to move away from the skin. Inaddition, because blood is a fluid, it responds differently than thesurrounding tissue to inertial effects, thus resulting in momentarychanges in volume at the point to which the sensor or probe is attached.

Noise (e.g., from patient movement) can degrade a CNIBP or pulseoximetry signal relied upon by a physician, without the physician'sawareness. This is especially true if the monitoring of the patient isremote, the motion is too small to be observed, or the doctor iswatching the instrument or other parts of the patient, and not thesensor site. Processing CNIBP or pulse oximetry (i.e., PPG) signals mayinvolve operations that reduce the amount of noise present in thesignals or otherwise identify noise components in order to prevent themfrom affecting measurements of physiological parameters derived from thePPG signals.

CNIBP monitoring system 10 may also include calibration device 80.Although shown external to monitor 14 in the example of FIG. 2,calibration device 80 may additionally or alternatively be internal tomonitor 14. Calibration device 80 may be connected to internal bus 50 ofmonitor 14. As described in more detail below, reference blood pressuremeasurements from calibration device 80 may be accessed bymicroprocessor 48 for use in calibrating the CNIBP measurements.

FIG. 3 is an illustrative processing system 300 in accordance with anembodiment. In an embodiment, input signal generator 310 generates aninput signal 316. As illustrated, input signal generator 310 may includeoximeter 320 (or similar device) coupled to sensor 318, which mayprovide as input signal 316, a PPG signal. It will be understood thatinput signal generator 310 may include any suitable signal source,signal generating data, signal generating equipment, or any combinationthereof to produce signal 316.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may pass light using a lightsource through blood perfused tissue and photoelectrically sense theabsorption of light in the tissue. For example, the oximeter may measurethe intensity of light that is received at the light sensor as afunction of time. A signal representing light intensity versus time or amathematical manipulation of this signal (e.g., a scaled versionthereof, a log taken thereof, a scaled version of a log taken thereof,etc.) may be referred to as the photoplethysmograph (PPG) signal. Inaddition, the term “PPG signal,” as used herein, may also refer to anabsorption signal (i.e., representing the amount of light absorbed bythe tissue) or any suitable mathematical manipulation thereof. The lightintensity or the amount of light absorbed may then be used to calculatethe amount of the blood constituent (e.g., oxyhemoglobin) being measuredas well as the pulse rate and when each individual pulse occurs.

In an embodiment, signal 316 may be coupled to processor 312. Processor312 may be any suitable software, firmware, and/or hardware, and/orcombinations thereof for processing signal 316. For example, processor312 may include one or more hardware processors (e.g., integratedcircuits), one or more software modules, computer-readable media such asmemory, firmware, or any combination thereof. Processor 312 may, forexample, be a computer or may be one or more chips (i.e., integratedcircuits). Processor 312 may perform some or all of the calculationsassociated with the blood pressure monitoring methods of the presentdisclosure. For example, processor 312 may determine the timedifference, T, between any two chosen characteristic points of a PPGsignal obtained from input signal generator 310. Processor 312 may alsobe configured to apply equation (1) (or any other blood pressureequation using an elapsed time value) and compute estimated bloodpressure measurements on a continuous or periodic basis. Processor 312may also perform any suitable signal processing of signal 316 to filtersignal 316, such as any suitable band-pass filtering, adaptivefiltering, closed-loop filtering, and/or any other suitable filtering,and/or any combination thereof. For example, signal 316 may be filteredone or more times prior to or after identifying characteristic points insignal 316.

Processor 312 may be coupled to one or more memory devices (not shown)or incorporate one or more memory devices such as any suitable volatilememory device (e.g., RAM, registers, etc.), non-volatile memory device(e.g., ROM, EPROM, magnetic storage device, optical storage device,flash memory, etc.), or both. Processor 312 may be coupled to acalibration device (not shown) that may generate or receive as inputreference blood pressure measurements for use in calibrating CNIBPcalculations.

Processor 312 may be coupled to output 314. Output 314 may be anysuitable output device such as, for example, one or more medical devices(e.g., a medical monitor that displays various physiological parameters,a medical alarm, or any other suitable medical device that eitherdisplays physiological parameters or uses the output of processor 212 asan input), one or more display devices (e.g., monitor, PDA, mobilephone, any other suitable display device, or any combination thereof),one or more audio devices, one or more memory devices (e.g., hard diskdrive, flash memory, RAM, optical disk, any other suitable memorydevice, or any combination thereof), one or more printing devices, anyother suitable output device, or any combination thereof.

It will be understood that system 300 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal generator 310 may beimplemented as parts of sensor 12 and monitor 14 and processor 312 maybe implemented as part of monitor 14. In one suitable approach, portionsof system 300 may be configured to be portable. For example, all or apart of system 300 may be embedded in a small, compact object carriedwith or attached to the patient (e.g., a watch (or other piece ofjewelry) or cellular telephone). In such embodiments, a wirelesstransceiver (not shown) may also be included in system 300 to enablewireless communication with other components of system 10. As such,system 10 may be part of a fully portable and continuous blood pressuremonitoring solution.

According to the present disclosure, reliable blood pressuremeasurements may be derived from a PPG signal obtained from a singlesensor or probe. In one suitable approach, the constants a and b inequation (1) above may be determined by performing a calibration. Thecalibration may involve taking a reference blood pressure reading toobtain a reference blood pressure P₀, measuring the elapsed time T₀corresponding to the reference blood pressure, and then determiningvalues for both of the constants a and b from the reference bloodpressure and elapsed time measurement. Calibration may be performed atany suitable time (e.g., once initially after monitoring begins) or onany suitable schedule (e.g., a periodic or event-driven schedule). Inone suitable approach, constants a and b in equation (1) above may bepredetermined—for example, determined based on empirical data withoutperforming a calibration.

In one suitable approach, the calibration may include performingcalculations mathematically equivalent to

$\begin{matrix}{{a = {c_{1} + \frac{c_{2}\left( {P_{0} - c_{1}} \right)}{{\ln\left( T_{0} \right)} + c_{2}}}}{and}} & (2) \\{b = \frac{P_{0} - c_{1}}{{\ln\left( T_{0} \right)} + c_{2}}} & (3)\end{matrix}$to obtain values for the constants a and b, where c₁ and c₂ arepredetermined constants that may be determined, for example, based onempirical data.

In other embodiments, determining the plurality of constant parametersin the multi-parameter equation (1) may include performing calculationsmathematically equivalent toa=P ₀−(c ₃ T ₀ +c ₄)ln(T ₀)  (4)andb=c ₃ T ₀ +c ₄  (5)where a and b are first and second parameters and c₃ and c₄ arepredetermined constants that may be determined, for example, based onempirical data.

In one suitable approach, the multi-parameter equation (1) may include anon-linear function which is monotonically decreasing and concave upwardin a manner specified by the constant parameters.

As mentioned above, multi-parameter equation (1) may be used todetermine estimated blood pressure measurements from the timedifference, T, between two or more characteristic points of a PPGsignal. In one suitable approach, the PPG signals used in the CNIBPmonitoring techniques described herein are generated by a pulse oximeteror similar device.

The present disclosure may be applied to measuring systolic bloodpressure, diastolic blood pressure, mean arterial pressure (MAP), or anycombination of the foregoing on an on-going, continuous, or periodicbasis. In one suitable approach, measuring the time difference, T,includes measuring a first time difference, T_(s), for certain portions(i.e., portions corresponding generally to the parts of the signalsassociated with systolic blood pressure) of the PPG signal. Measuringthe first time difference may comprise maximizing a cross-correlationbetween some components of the PPG signal. In such measurements,portions of the PPG signal that fall below a first threshold may not beconsidered in one suitable approach. The first threshold may be anaverage value for the signal (or equivalently a mean value for thesignal).

FIG. 4 shows illustrative PPG signal 400. As described above, in onesuitable approach PPG signal 400 may be generated by a pulse oximeter orsimilar device positioned at any suitable location of a subject's body.Notably, PPG signal 400 may be generated using only a single sensor orprobe attached to the subject's body. Such techniques are described withrespect to U.S. patent application Ser. No. 12/242,238, filed on Sep.30, 2008, entitled “Systems and Methods for Non-Invasive Blood PressureMonitoring” and U.S. patent application Ser. No. 12/242,867, filed onSep. 30, 2008, entitled “Systems And Methods For Non-Invasive ContinuousBlood Pressure Determination,” which are both hereby incorporated byreference herein in their entireties.

Characteristic points in a PPG (e.g., PPG signal 400) may be identifiedin a number of ways. For example, in one suitable approach, the turningpoints of 1st, 2nd, 3rd (or any other) derivative of the PPG signal areused as characteristic points. Additionally or alternatively, points ofinflection in the PPG signal (or any suitable derivative thereof) mayalso be used as characteristic points of the PPG signal. The timedifference, T, may correspond to the time it takes the pulse wave totravel a predetermined distance (e.g., a distance from the sensor orprobe to a reflection point and back to the sensor or probe).Characteristic points in the PPG signal may also include the timebetween various peaks in the PPG signal and/or in some derivative of thePPG signal. For example, in one suitable approach, the time difference,T, may be calculated between (1) the maximum peak of the PPG signal inthe time domain and the second peak in the 2nd derivative of the PPGsignal (the first 2nd derivative peak may be close to the maximum peakin the time domain) and/or (2) peaks in the 2nd derivative of the PPGsignal. Any other suitable time difference between any suitablecharacteristic points in the PPG signal (e.g., PPG signal 400) or anyderivative of the PPG signal may be used as T in other embodiments.

In one suitable approach, the time difference between the adjacent peaksin PPG signals, the time difference between the adjacent valleys in PPGsignals, or the time difference between any combination of peaks andvalleys, can be used as the time difference T. As such, adjacent peaksand/or adjacent valleys in PPG signals (or in any derivative thereof)may also be considered characteristics points. In one suitable approach,these time differences may be divided by the actual or estimated heartrate to normalize the time differences. In one suitable approach, theresulting time difference values between two peaks may be used todetermine the systolic blood pressure, and the resulting time differencevalues between two valleys may be used to determine the diastolic bloodpressure. In an embodiment, the time differences between characteristicpoints associated with a pulse's maximal and minimal turning points(i.e., those characteristic points associated with maximum and minimumpressures) may be measured from relatively stable points in PPG signals.

A patient's blood pressure may be monitored continuously using movingPPG signals. PPG signal detection means may include a pulse oximeter (orother similar device) and associated hardware, software, or both. Aprocessor may continuously analyze the signal from the PPG signaldetection means in order to continuously monitor a patient's bloodpressure.

In one suitable approach, past blood pressure measurements are used toscale current and future measurements. For example, to avoid largeswings in detected blood pressure a running or moving blood pressureaverage may be maintained. Detected blood pressure values outside somepre-defined threshold of the moving average may be ignored in onesuitable approach. Additionally or alternatively, detected bloodpressure values outside some pre-defined threshold of the moving averagemay automatically signal a recalibration event.

According to one suitable approach, one or more calibration (orrecalibration) steps may be employed by measuring the patient's bloodpressure (or a reference blood pressure), P₀, and then measuring thecorresponding elapsed time, T₀, between the chosen characteristic pointsin the PPG signal. Updated or refined values for constants a and b ofequation (1) (or other suitable blood pressure equation) may then becomputed based on the calibration. Calibration may be performed once,initially at the start of the continuous monitoring, or calibration maybe performed on a regular or event-driven schedule. In one suitableapproach, calibration may also include changing the characteristicpoints used to compute the time difference, T. For example, severaldifferent blood pressure determinations may be made in parallel usingdifferent sets of characteristic points. The set of characteristicpoints that yields the most accurate blood pressure reading during thecalibration period may then be used as the new set of characteristicpoints. As such, the characteristic points of the PPG signal used in theblood pressure determination may be modified on-the-fly and may varyduring a single monitoring session. Such an adaptive approach toselecting characteristic points in the PPG signal may help yield moreaccurate blood pressure readings. In other embodiments, no calibrationsteps are performed in order to yield accurate blood pressuremeasurements, as will be described below.

In one suitable approach, no calibration steps are performed in order toyield accurate blood pressure measurements. It may be important tomonitor certain physiological parameters of a patient, such asrespiration rate and blood pressure, in a clinical setting. For example,blood pressure information may be important for diagnosing or monitoringa cardiovascular ailment. However, blood pressure measurements need notrely on an initial calibration measurement taken by additionalequipment, such as a non-invasive blood pressure cuff, as will bedescribed with respect to the embodiments that follow.

In an embodiment, a blood pressure measurement may be obtained based ona localized change in a related indicator, such as differential pulsetransit time (DPTT). These changes in DPTT may directly translate intochanges in blood pressure and may be used to generate CNIPB readings. Inone suitable approach, the blood pressure measurement may be provided inabsolute units of pressure (e.g., mmHG or cmH₂O). Such a measurement maybe more convenient for clinicians and/or more useful in furthercalculations by monitoring equipment. As discussed above, no bloodpressure measurement device (e.g., a non-invasive blood pressure cuff)may be required to obtain a measure of blood pressure from an initialcalibration measurement. The only probes that may be required for themeasurement of blood pressure fluctuations due to respiration are one ormore oximeter probes attached to one or more sites on the body of thepatient at particular distances from the heart (e.g., at the fingerand/or the forehead). Such probes may be substantially similar to sensor12 (FIG. 1). In one suitable approach, respiratory effort may bedetermined from the changes in DPTT. Although the present disclosurewill be described with respect to the measurement of respiratory effort(e.g., as derived from changes in blood pressure), it will be understoodthat the present disclosure may be applied to any suitable physiologicalparameter (e.g., respiration rate, blood oxygen saturation) and may beused to determine a characteristic value of that physiologicalparameter. Embodiments will now be discussed in connection with FIGS.5-9.

The respiratory effort of a patient may be derived from any suitablereceived signal or signals using, for example, system 10 or system 400.FIGS. 5( a), 5(b), 6, and 7 show illustrative plots 510, 520, 600, and700 of DPTT measurements in accordance with embodiments of thedisclosure. In one suitable approach, plots 510, 520, 600, and 700 maybe produced from data gathered from an individual probe or sensor (i.e.,sensor 12) used with a detector (i.e., detector 18) suitably positionedanywhere on patient 40 (e.g., in an area where a strong pulsatile flowmay be detected, such as over arteries in the neck, wrist, thigh, ankle,ear, or any other suitable location).

In addition, in one suitable approach the data may be gathered while thepatient is breathing against a slight resistance in order to cause morepronounced changes in DPTT within a respiratory cycle. These pronouncedchanges are then used to determine relative changes in blood pressure.The data may be captured in a PPG signal, which may then be analyzed(i.e., using processor 412) and used to compute DPTT measurements. Asshown in plots 510 and 520, the DPTT measurements may be plotted overtime, with DPTT measurements from diastolic and systolic periods of aheart beat each plotted separately. These sets of DPTT measurements maybe referred to as “systolic DPTT measurements” and “diastolic DPTTmeasurements”, respectively. In one suitable approach (not shown), DPTTmeasurements may be related to thoracic pressure changes. In onesuitable approach, these plots may contain continuous representations(e.g., signals) of DPTT measurements over time. For example, plot 510depicts systolic DPTT measurements as signal 505, and plot 520 depictsdiastolic DPTT measurements as signal 525. In addition, plots 600 and700 depict diastolic or systolic DPTT measurements as signals 610 and702, respectively.

In one suitable approach, signals 505, 525, 610, and 702 may bemodulated based on a patient's breathing (e.g., the baseline of signals505, 525, 610, and 702 may oscillate in relation to the patient'sbreathing) and may include other oscillatory features (e.g., may containrepeating patterns of DPTT values) that may be analyzed to derive ameasure of respiratory effort. In one suitable approach, multiparameterpatient monitor 26 may be configured to display plots 510, 520, 600,and/or 700.

From these DPTT measurements, reliable and accurate blood pressurevalues may be computed on a continuous or periodic basis, as will bedescribed below. In an embodiment, the changes in blood pressuredirectly correlate to the respiratory effort of a patient. For example,a patient's change in blood pressure may be taken as a change in therespiratory effort of the patient. Thus, the changes in DPTT over timemay be used to determine relative changes in respiratory effort. In anexample, the change in blood pressure over a respiratory cycle may bedetermined based on characteristic measurements (e.g., maximum andminimum measurements) of DPTT during that respiratory cycle. In turn,the respiratory effort may be determined to be directly correlated tothese changes in DPTT—for example, the larger difference between themaximum and minimum DPTT during the respiratory cycle, the higher themeasurement of respiratory effort. In another example, the mean DPTTvalue over a period of time may be used to determine a change in bloodpressure—for example, a larger mean DPTT value over a first period oftime may correlate to a blood pressure measurement that is lowerrelative to an initial blood pressure measurement. In turn, therespiratory effort of a patient during the first period of time may bedetermined to be higher than during the period of time associated withthe initial blood pressure measurement.

Focusing now on plots 510 and 520 of FIGS. 5( a) and 5(b), in order tocalculate respiratory effort from DPTT measurements, values of signals505 and 525 may be selected. This selection may be based on identifiedcharacteristic points (e.g., maximum, minimum) for blood pressurecalculations. These calculations may include, for example, taking anatural logarithm of a time difference between two characteristicpoints, or by solving a multi-parameter equation, such as p=a+b·ln(T),or a mathematical equivalent thereof, where p is the determined bloodpressure measurement, T is a time difference determined from theidentified characteristic points, and a and b are constants.

In an embodiment, a multi-parameter equation (i.e., equation (1)) may beadopted to calculate the difference between two readings of DPTT suchthat no blood pressure calibrations need be performed—i.e., the bloodpressure calculation is performed using only the DPTT readings. Forexample, the multi-parameter equation may be adopted for two readings ofDPTT taken at two different times—e.g.,P ₀ =a+b·ln(T ₀)andP ₁ =a+b·ln(T ₁)  (6),where P₀ and P₁ are relative blood pressure measurements, T₀ and T₁ areDPTTs determined from the identified characteristic points, a is aconstant, and b is proportional to the times T₀ and T₁. Accordingly, thechange in pressure may be calculated asΔP=b·(ln(T ₀)−ln(T ₁))  (7).In an embodiment, b=c3·T+c4, where T is the average of T₀ and T₁, and c3and c4 are pressure constants. In such embodiments, the equation forchange in blood pressure may be rewritten asΔP=(c ₃·((T ₀ +T ₁)/2)+c ₄)·(ln(T ₀)−ln(T ₁))  (8).In other embodiments, T may be any other suitable DPTT's that may, forexample, be proportional to T₀ and T₁, including the values of T₀ and T₁themselves.

In an embodiment, the identified DPTTs of characteristic points used inblood pressure calculation may be selected according to the maximum andminimum DPTT values throughout an entire respiration cycle. Exemplaryrespiration cycles are illustrated in portion 514 of signal 505 andportion 524 of signal 525. In an embodiment, a respiration cycle may beobserved using a moving window of time of any suitable length—forexample, 1, 1.5, 3, 5, or any suitable number of seconds. The maximumand minimum DPTT values may be selected over a respiratory cycle becauseof the stability and accuracy of such values as compared to the frequentand/or erratic localized oscillations in DPTT throughout a single cycle(e.g., the localized fluctuations illustrated in portion 514 of signal505 and portion 524 of signal 525). In an embodiment, the maximum andminimum DPTT values within a respiration cycle may be determined usingany suitable combination of signal processing techniques for determiningfiducial points within a signal, including transformation, manipulation,and/or filtering techniques. Such techniques are described with respectto FIG. 3 and in, for example, U.S. patent application No. 61/369,452,filed Jul. 30, 2010, entitled “Systems and Methods for ProcessingMultiple Physiological Signals, which is hereby incorporated byreference herein in its entirety.

In an embodiment, changes in systolic and diastolic pressure may becalculated using the equationΔP=(c ₃·((T ₀ +T ₁)/2)+c ₄)·(ln(T ₀)−ln(T ₁))  (9)and substituting in different sets of constants for c₃ and c dependingon whether systolic pressure or diastolic pressure is being calculated.For example, when a change in systolic pressure is calculated, c₃ may beset to 0.44 while c₄ is set to −9.1. In addition, when a change indiastolic pressure is calculated, c₃ may be set to −0.26 while c₄ is setto −4.4. In an embodiment, constants c₃ and c₄ may be selected separatefrom any blood pressure calibration reading. For example, with referenceto FIG. 5A, signal 505 may be observed over portion 514. Using suitablesignal processing techniques, the maximum systolic DPTT 511 may bedetermined to be 50.5 milliseconds, and a minimum systolic DPTT 513 maybe determined to be 40 milliseconds. Applying these measurements to theequation for ΔP with c₃ set to 0.44 and c₄ set to −9.1, the change insystolic blood pressure is calculated to be 6.76 mmHg. Similarly, withreference to FIG. 5B, signal 525 may be observed over portion 524. Usingsuitable signal processing techniques, the maximum diastolic DPTT 521may be determined to be 55.5 milliseconds, and a minimum diastolic DPTT523 may be determined to be 49.5 milliseconds. Applying thesemeasurements to the equation for ΔP with c₃ set to −0.26 and c₄ set to−4.4, the change in diastolic blood pressure is calculated to be 2.06mmHg. In an embodiment (not shown), identified characteristic points T₀and T₁ used in blood pressure calculation may be selected according tothe maximum and minimum DPTT values of a DPTT signal associated withthoracic DPTT measurements. Changes in thoracic blood pressure may becalculated using the equation (9) in a manner similar to the calculationdescribed with respect to systolic and diastolic blood pressure aboveusing the constants c₃ and c₄. The constants may be further optimizedfor the changes in thoracic pressure associated with respiratory effortderived from empirical data.

In an embodiment, the change in blood pressure calculated using equation(9) directly correlates to the respiratory effort of a patient. Thus,the changes in DPTT over a respiratory cycle may be used to determinerelative changes in respiratory effort. In an example, the change inrespiratory effort may be calculated based on a change in blood pressurecalculated according to the maximum and minimum DPTT values selectedthroughout a respiration cycle. For example, a higher measurement ofrespiratory effort may be calculated the larger the difference thebetween the maximum and minimum DPTT during the respiratory cycle. Themaximum and minimum DPTT values may be selected according to the methodsdiscussed FIGS. 5( a) and 5(b).

In an embodiment, the window in which maximum and minimum DPTTmeasurements are taken may be reduced in size (e.g., from 3 seconds to1.5 seconds) such that the respiration modulation can be better observedin the signal. In addition, the resolution of signals 505 and 525 may beincreased (e.g., by increasing the reducing the reporting increment from0.4 seconds to 0.1 seconds) to allow for a higher resolution ofrespiratory modulations.

In an embodiment, calculated changes in diastolic and systolic bloodpressure measurements may be used in combination to obtain a change inMAP. The MAP may be calculated according to a weighted average ofcontemporaneous changes in diastolic, systolic, and/or thoracicpressure. For example, the change in mean arterial pressure may becalculated according toΔP _(map)=(ΔP _(sys)+2ΔP _(dia))/3  (10),where ΔP_(sys) and ΔP_(dia) are contemporaneous changes in systolic anddiastolic pressures, respectively. For example, if the change insystolic pressure is 6.76 mmHg and the change in diastolic pressure is2.06 mmgH, the change in the MAP according to equation (10) would be3.63 mmHg. In an embodiment, the MAP may be calculated based on aweighted average of contemporaneous changes in diastolic and thoracicpressure, systolic and thoracic pressure, or diastolic, systolic, andthoracic pressure. In an embodiment, the MAP may be calculated separatefrom any blood pressure calibration reading.

In an embodiment, the change in MAP calculated using equation (10)directly correlates to the respiratory effort of a patient. Thus, aweighted average of the changes in diastolic, systolic, and/or thoracicDPTT over a period of time may be used to determine relative changes inrespiratory effort. In an example, the change in respiratory effort maybe calculated based on a change in MAP, which includes a weightedaverage of the changes in diastolic and systolic blood pressures over arespiratory cycle. As discussed, this calculation of respiratory effortdoes not require calibration readings from a non-invasive blood pressurecuff.

FIG. 6 shows an illustrative plot 600 of a mean DPTT measurement 612associated with DPTT signal 610 over a first period of time inaccordance with an embodiment of the disclosure. As mentioned above,DPTT signal 610 may be representative of either systolic or diastolicDPTT measurements. In an embodiment, mean DPTT measurement 612 may becalculated using any suitable signal processing techniques, including,for example, any suitable filtering techniques such as low-passfiltering. The mean DPTT measurement 612 may be used to calculate achange in blood pressure along any point in DPTT signal 610, (i.e., arunning value of the fluctuation in systolic or diastolic pressure). Forexample, a running value of the fluctuation in systolic or diastolicpressure may be calculated according toΔP=(c ₃ ·T _(m) +c ₄)·ln(T)−ln(T _(m)))  (11),where T is any point along a DPTT signal (e.g., DPTT signal 610), T_(m)is the mean DPTT measurement associated with the DPTT signal, and c₃ andc₄ are pressure constants associated with calculation of changes indiastolic or systolic pressure (e.g., the pressure constants discussedabove with respect to FIGS. 5A and 5B). In this manner, a highlylocalized change in blood pressure may be obtained. In an embodiment,the fluctuation in systolic or diastolic pressure calculated in equation(11) directly correlates to the respiratory effort of a patient. Forexample, the blood pressure variation given by equation 11 may be takenas the respiratory effort of the patient.

FIG. 7 shows an illustrative plot 700 of mean DPTT measurements 710 and720 over multiple periods of time associated with a DPTT signal 702 inaccordance with an embodiment of the disclosure. In an embodiment, theequations discussed with respect to FIGS. 5( a), 5(b), and 6 may beadopted to calculate localized blood pressure changes other than thoseassociated with a single respiratory cycle (e.g., those changesdiscussed with respect to FIGS. 5( a) and 5(b)) or highly localizedfluctuations (e.g., those changes discussed with respect to FIG. 6).These changes in pressure may occur over short periods of respiratoryactivity, (e.g. seconds), or longer periods, (e.g. several seconds orminutes). For example, as illustrated in FIG. 7, a patient may bebreathing normally during first period of respiratory activity 706,transition into a period of restricted breathing activity during secondperiod of respiratory activity 707, and sustain a period of restrictedbreathing activity during third period of respiratory activity 708. Thetransition into and duration of the period of restricted breathingactivity may be due to increased autonomic and/or respiratory activityon behalf of the patient being monitored—for example, sitting up on abed or walking on a treadmill. In an embodiment, DPTT signal 702 maychange in baseline (i.e., mean DPTT) or amplitude during this increasedautonomic activity. For example, mean DPTT measurement 720 associatedwith signal 702 during third time period 708 may be lower than mean DPTTmeasurement 710 associated with signal 702 during first time period 706.In addition, the amplitude of signal 702 may be larger during third timeperiod 708 than during first time period 706.

In an embodiment, mean DPTT values may be calculated for several timeperiods of signal 702. For example, separate mean DPTT values may becalculated for first time period 706, second time period 707, and thirdtime period 708. These mean DPTT values may then be used to calculatelocalized changes in pressure due to increased autonomic activity. In anembodiment, this localized change in blood pressure may be calculatedduring time periods in which the mean DPTT is stable for a suitablethreshold amount of time. For example, the localized change in bloodpressure may be calculated as:ΔP _(loc)=(c ₃·((T _(m3) +T _(m4))/2)+c ₄)·(ln(T _(m3))−ln(T_(m4)))  (12)Where T_(m3) is the mean DPTT value of a first time period in which themean DPTT was stable (e.g., first time period 706), T_(m4) is the meanDPTT value of a second time period in which the mean DPTT was stable(e.g., third time period 708), and c₃ and c₄ are pressure constantsassociated with calculation of changes in diastolic or systolic pressure(e.g., the pressure constants discussed above with respect to FIGS. 5Aand 5B). In this manner, a change in blood pressure may be calculatedaccording to the changes in baseline of signal 702 rather than changesin amplitude. In one suitable approach, mean DPTT values may becalculated for several time periods of several DPTT signals, and thenused to calculate localized changes in pressure according to equation(12) above.

In an embodiment, the change in blood pressure calculated using equation(12) directly correlates to the respiratory effort of a patient. Thus,the changes in mean DPTT over periods of time may be used to determinerelative changes in respiratory effort.

In an embodiment, it will be understood that the calculation of changesin systolic and/or diastolic blood pressure described with respect toFIGS. 5( a), 5(b), 6, and 7 need not depend on any blood pressurecalibration reading. Further, in an embodiment, it will be understoodthat changes in blood pressure may be determined according to thecalculations described with respect FIGS. 5( a), 5(b), 6, and 7 usingany suitable relationship between pressure and changes in DPTT—such as arelationship other than the logarithmic relationship established in theequations discussed above, (e.g., a linear relationship).

FIG. 8 is a flowchart 800 of illustrative steps for computing a physicalparameter based on a localized change in DPTT in accordance with anembodiment of the disclosure. Flow chart 800 may be performed byprocessor 412 (FIG. 4) or microprocessor 48 (FIG. 2) in real time usinga PPG signal obtained by sensor 12 (FIG. 2) or input signal generator410 (FIG. 4), which may be coupled to patient 40, using a time windowsmaller than the entire time window over which the PPG signal may becollected. Alternatively, flow chart 800 may be performed offline on PPGsignal samples from QSM 72 (FIG. 2) or from PPG signal samples stored inRAM 54 or ROM 52 (FIG. 2), using the entire time window of data overwhich the PPG signal was collected.

Process 800 may begin at step 810, in which PPG signals may be collectedby sensor 12 or input signal generator 410 over any suitable time periodt to compute changes in physical parameters. In an embodiment, PPGsignals may be collected corresponding to sensors located at differentdistances from the heart (e.g., at the finger and forehead). Thesesignals may include a baseline signal that may fluctuate due to thebreathing of patient 40, which may cause the PPG signals to oscillate,or twist, in the time plane. For example, the PPG signals may experienceamplitude modulation that may be related to dilation of the patient'svessels in correspondence with the patient's respiration. In addition,the PPG signals may experience baseline modulation that may be relatedto the breathing effort of the patient in correspondence to breathingrestrictions or stresses placed on the patient (e.g., exercise). At step820, DPTT signals may be calculated based on the signals received atstep 810. In an embodiment, separate DPTT signals may be calculated toreflect diastolic and systolic periods of respiration. The DPTT signalsmay be calculated using any suitable combination of signal processingtechniques, such as performing a maximum correlation algorithm based onderivatives calculated from the PPG signals, and/or using a degree ofconfidence calculation. Such techniques are further described in U.S.patent application Ser. No. 12/847,546, filed Jul. 30, 2010, entitled“Systems and Methods for Improved Computation of Differential PulseTransit Time from Photoplethysmograph Signals, which is herebyincorporated by reference herein in its entirety. In an embodiment, theresolution of the calculated DPTT signals may be increased (e.g., byincreasing the reducing the reporting increment from 0.4 seconds to 0.1seconds) to allow for a higher resolution of respiratory modulations.

At step 830, localized changes in physical parameters may be calculatedbased on selected characteristic points in the DPTT signals. In anembodiment, the characteristic points may be selected according to thedesired type of localized change in the physical parameter. For example,localized changes in diastolic or systolic blood pressure may becalculated according to selected maximum and minimum points within awindowed portion of the DPTT signals by using equation (9). The windowedportion may correspond to a single respiratory cycle of patient 40. Inan embodiment, separate calculations may be performed using diastolicand systolic DPTT signals to determine changes in diastolic and systolicpressure. In an embodiment, both diastolic and systolic DPTT signals maybe used to calculate a MΔP (e.g., by using equation (10)). A fluctuationin blood pressure may be calculated using any selected point in the DPTTsignal along with a mean DPTT value associated with the DPTT signal(e.g., by using equation (11)). Other localized blood pressure changesmay be calculated using several mean DPTT values from different portionsof DPTT signals (e.g., a portion where patient 40 is resting and aportion where patient 40 is exercising) by using equation (12). After ablood pressure measurement is determined at step 830, process 800 mayrepeat step 830 using different characteristic points to calculatedifferent changes in blood pressure. As such, process 800 may generateblood pressure measurements continuously. In an embodiment, thecalculated change in blood pressure may be independent from any bloodpressure calibration, as the only parameters used to calculate bloodpressure from the patient are derived from DPTT signals themselves. Inan embodiment, the calculated change in blood pressure may be inabsolute units of pressure (e.g., mmHG or cmH₂O). In an embodiment, thecalculated changes in blood pressure directly correlate to therespiratory effort of a patient. Thus, the calculated changes in bloodpressure may be used to calculate relative changes in respiratoryeffort. In an embodiment, the calculated changes in blood pressure maybe taken to be the respiratory effort of a patient. Step 830 isdescribed in greater detail with respect to the steps of process 900 ofFIG. 9 below.

At step 840, the calculated physical parameter may be output in anysuitable fashion, for example by storing or displaying the calculatedphysical parameter. For example, multi-parameter patient monitor 26(FIG. 1) may display a patient's blood pressure on display 28 (FIG. 1).Additionally or alternatively, the measurements may be saved to memoryor a storage device (e.g., ROM 52 or RAM 54 of monitor 14 (FIG. 2)) forlater analysis or as a log of a patient's medical history. Process 800may then proceed to step 850 and end.

FIG. 9 is a flowchart 900 of illustrative steps for computing anabsolute pressure measurement based on a localized change in DPTT inaccordance with an embodiment of the disclosure. In an embodiment, thesteps of process 900 may be executed as part of step 830 of process 800(FIG. 8). Process 900 may be performed by processor 412 (FIG. 4) ormicroprocessor 48 (FIG. 2) substantially similarly to process 800.Process 900 may begin at step 910. At step 910, pressure calculationcoefficients may be selected for use in the computation of absoluteblood pressure. In an embodiment, the pressure calculation coefficientsmay be selected according to the desired type of pressure calculationand/or the DPTT signals available for pressure calculation (e.g., theDPTT signals calculated at step 820 by processor 412 (FIG. 4) ormicroprocessor 48 (FIG. 2)). For example, the pressure calculationcoefficients may be selected based on whether the desired pressurecalculation is a diastolic, systolic, or thoracic blood pressuremeasurement, and/or whether the available DPTT signals are associatedwith DPTT measurements from diastolic, systolic, or thoracic periods ofa heart beat. Suitable pressure calculation coefficients for systolicand diastolic blood pressure calculation may be substantially similar tothe pressure constants discussed with respect to equation (9) as well asFIGS. 5A and 5B.

At step 920, a window, or portion, of the DPTT signals available forpressure calculation may be selected for use in the computation ofabsolute blood pressure. In an embodiment, the window may be selectedaccording to the desired degree of localization of the computation ofabsolute blood pressure. For example, if the desired degree oflocalization of the blood pressure measurement is an entire respirationcycle, the window may be selected as a portion of the DPTT signal thatreflects an entire respiration cycle, such as portion 514 (FIG. 5A). Asanother example, if the desired degree of localization of the bloodpressure measurement is high, the window may be selected to be aninfinitesimal slice of the DPTT signal, such as a collection of pointsof the DPTT signal. Finally, if the desired degree of localization ofthe blood pressure measurement is low, the window may be selected to bea portion of the DPTT signal that reflects several respiration cycles,such as 2, 3, 5, 10, or any number of suitable respiration cycles. In anembodiment, several discontinuous portions of the available DPTT signalsmay be selected for the blood pressure calculation window such thatthere are multiple blood pressure calculation windows.

At step 930, characteristic points of the DPTT signal may be selectedwithin the blood pressure calculation windows for use in the computationof absolute blood pressure. In an embodiment, the characteristic pointsmay be selected based on distinguishing features of the selectedportions of the DPTT signals, such as maximum, minimum, or mean DPTTvalues within the blood pressure calculation window. Thesecharacteristic points may be useful in the calculation of blood pressureaccording to equation (9) or (10). In an embodiment, the characteristicpoints may be calculated based on a mean value of a portion of thepoints of the DPTT signal within the blood pressure calculation window.These characteristic points may be useful in the calculation of bloodpressure according to equation (11). In an embodiment, thecharacteristic points may be selected based on a desired localizedchange in blood pressure—e.g., at a point in which the baseline of theDPTT signal is stable, such as when the patient is in a state ofexercise or in a state of rest, but not in transition between the two.These characteristic points may be useful in the calculation of bloodpressure according to equation (12).

At step 940, an absolute blood pressure measurement is calculated basedon the pressure calculation coefficients selected at step 910 and thecharacteristic points selected at step 930. These calculations may beperformed according to equations (9) through (12) substantially similarto the calculations described with respect to step 830. After a bloodpressure measurement is determined at step 940, process 900 may repeatsteps 810, 820, and 830, to select different pressure coefficients andcharacteristic points to calculate different changes in blood pressure.As such, process 900 may generate blood pressure measurementscontinuously. In an embodiment, the calculated change in blood pressuremay be independent from any blood pressure calibration, as the onlyparameters used to calculate blood pressure from the patient are derivedfrom DPTT signals themselves.

At step 950, respiratory effort is calculated based on the absoluteblood pressure measurements calculated at step 940. These calculationsmay be based on, for example, a direct correlation between bloodpressure and respiratory effort (e.g., those correlations discussed withrespect to FIGS. 5( a), 5(b), 6, and 7. In an example, a highermeasurement of respiratory effort may be calculated the larger thedifference the between the maximum and minimum DPTT during therespiratory cycle. In addition, a higher measurement of respiratoryeffort may be calculated the larger the difference between a lower meanDPTT value over a first period of time as compared to a higher mean DPTTvalue over a second period of time. In an embodiment, the calculatedchanges in blood pressure may be taken to be the respiratory effort of apatient.

In practice, one or more steps of processes 800 and 900 may be combinedwith other steps, performed in any suitable order, performed in parallel(e.g., simultaneously or substantially simultaneously), or removed.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications can be made by those skilled in theart without departing from the scope and spirit of the disclosure. Theabove described embodiments are presented for purposes of illustrationand not of limitation. The present disclosure also can take many formsother than those explicitly described herein. Accordingly, it isemphasized that the disclosure is not limited to the explicitlydisclosed methods, systems, and apparatuses, but is intended to includevariations to and modifications thereof which are within the spirit ofthe following claims.

What is claimed is:
 1. A method for calculating a measure of respiratoryeffort of a subject, the method comprising: receiving twophotoplethysmograph (PPG) signals corresponding to two differentlocations on the subject; calculating, using electronic processingequipment, differential pulse transit time (DPTT) values for the two PPGsignals, wherein the DPTT values represent differences in arrival timesbetween the two locations; determining, using the electronic processingequipment, a measure of respiratory effort based at least in part on thecalculated DPTT values; and storing, using a storage device, the measureof respiratory effort.
 2. The method of claim 1, wherein the determiningfurther comprises: selecting a plurality of pressure calculationcoefficients based on the calculated DPTT values; selecting a pluralityof characteristic DPTT values; determining, based at least in part onthe selected pressure calculation coefficients and selectedcharacteristic DPTT values, a measure of blood pressure; andcalculating, based at least in part on the measure of blood pressure, ameasure of respiratory effort.
 3. The method of claim 2, wherein themeasure of blood pressure is determined solely based on the selectedpressure calculation coefficients and selected characteristic DPTTvalues.
 4. The method of claim 2, further comprising: selecting at leasta portion of the calculated DPTT values corresponding to at least onerespiratory cycle of the subject; and selecting a plurality ofcharacteristic DPTT values within the portion of the DPTT values.
 5. Themethod of claim 4, wherein the characteristic DPTT values are selectedbased on at least a maximum DPTT value and a minimum DPTT value in theportion of the DPTT values.
 6. The method of claim 4, further comprisingcalculating a mean DPTT value corresponding to the at least onerespiratory cycle of the subject.
 7. The method of claim 2, furthercomprising: selecting at least a portion of the calculated DPTT valuescorresponding to at least two periods of respiratory activity;calculating a mean DPTT value corresponding to each of the at least twoperiods of respiratory activity; and determining, based at least in parton the selected pressure calculation coefficients and calculated meanDPTT values, the measure of blood pressure.
 8. The method of claim 7,wherein the at least two periods of respiratory activity correspond toat least one period in which the subject is exercising and at least oneperiod in which the subject is resting.
 9. The method of claim 1,wherein the calculation of the measure of respiratory effort is based ona linear relationship between a measure of blood pressure andrespiratory effort.
 10. A system for calculating a measure ofrespiratory effort of a subject, comprising: an input for receiving twophotoplethysmograph (PPG) signals corresponding to two differentlocations on the subject; a processor configured to use at leastportions of the two PPG signals to: calculate differential pulse transittime (DPTT) values for the two PPG signals, wherein the DPTT valuesrepresent differences in arrival times between the two locations; anddetermine a measure of respiratory effort based at least in part on thecalculated DPTT values; and a storage device for storing the measure ofrespiratory effort.
 11. The system of claim 10, wherein the processor isconfigured to use at least a portion of the calculated DPTT values to:select a plurality of pressure calculation coefficients based on thecalculated DPTT values; select a plurality of characteristic DPTTvalues; determine, based at least in part on the selected pressurecalculation coefficients and selected characteristic DPTT values, ameasure of blood pressure; and calculate, based at least in part on themeasure of blood pressure, a measure of respiratory effort.
 12. Thesystem of claim 11, wherein the measure of blood pressure is determinedsolely based on the selected pressure calculation coefficients andselected characteristic DPTT values.
 13. The system of claim 11, whereinthe processor is configured to use at least a portion of the calculatedDPTT values to: select at least a portion of the calculated DPTT valuescorresponding to at least one respiratory cycle of the subject; andselect a plurality of characteristic DPTT values within the portion ofthe DPTT values.
 14. The system of claim 13, wherein the characteristicDPTT values are selected based on at least a maximum DPTT value and aminimum DPTT value in the portion of the DPTT values.
 15. The system ofclaim 13, wherein the processor is further configured to calculate amean DPTT value corresponding to the at least one respiratory cycle ofthe subject.
 16. The system of claim 11, wherein the processor isconfigured to use at least a portion of the calculated DPTT values to:select at least a portion of the calculated DPTT values corresponding toat least two periods of respiratory activity; calculate a mean DPTTvalue corresponding to each of the at least two periods of respiratoryactivity; and determine, based at least in part on the selected pressurecalculation coefficients and calculated mean DPTT values, the measure ofblood pressure.
 17. The system of claim 16, wherein the at least twoperiods of respiratory activity correspond to at least one period inwhich the subject is exercising and at least one period in which thesubject is resting.
 18. The system of claim 10, wherein the calculationof the measure of respiratory effort is based on a linear relationshipbetween a measure of blood pressure and respiratory effort. 19.Non-Transitory computer readable storage media comprising instructionsfor: receiving two photoplethysmograph (PPG) signals corresponding totwo different locations on the subject; calculating differential pulsetransit time (DPTT) values for the two PPG signals, wherein the DPTTvalues represent differences in arrival times between the two locations;determining a measure of respiratory effort based at least in part onthe calculated DPTT values; and storing the measure of respiratoryeffort.